Inference for covariates that accounts for ascertainment and random genetic effects in family studies

• 成果类型: Article
• 署名作者: Pfeiffer, RM; Gail, MH; Pee, D
• 署名单位: National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); Information Management Services, Inc.
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/88.4.933
• 发表日期: 2001
• 页码: 933948
• 关键词: nasopharyngeal carcinoma; EFFICIENCY

摘要

Family studies to identify disease-related genes often collect families with multiple cases. If environmental exposures or other measured covariates are also important, they should be incorporated into these genetic analyses to control for confounding and increase statistical power. We propose a two-level mixed effects model that allows us to estimate environmental effects while accounting for varying genetic correlations among family members and adjusting for ascertainment by conditioning on the number of cases in the family. We describe a conditional maximum likelihood analysis based on this model. When genetic effects are negligible, this conditional likelihood reduces to standard conditional logistic regression. We show that the simpler conditional logistic regression typically yields biased estimators of exposure effects, and we describe conditions under which the conditional logistic approach has little or no bias.